Linganisha mbinu

	Uchanganuzi wa Uenezaji wa Mitandao yenye Uzito	Upekee wa Kati wa Uzito (Weighted Betweenness Centrality)
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2004	2010
Mwanzilishi	Barrat, A.; Newman, M. E. J.	Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)
Aina	Network diffusion model	Centrality measure (path-based)
Chanzo asilia	Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.	Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
Majina mbadala	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	WBC, weighted shortest-path betweenness, edge-weighted betweenness, geodesic betweenness (weighted)
Zinazohusiana	6	6
Muhtasari	Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks.	Weighted Betweenness Centrality extends Freeman's betweenness measure to edge-weighted graphs by routing shortest paths through a tunable transformation of edge weights. Nodes that sit on many high-value shortest paths receive high scores, identifying brokers and bridges in social, biological, and information networks where tie strength matters.